POS0422 METABOLOMIC FINGERPRINT TO DISCRIMINATE REMISSION FROM ACTIVE DISEASE AND HEALTHY CONTROLS IN PSORIATIC ARTHRITIS
Authors: M. M. Angioni, C. Piras, A. Floris, V. P. Leoni, M. Congia, E. Chessa, M. Piga, L. Atzori, A. Cauli
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Among the unmet needs in psoriatic arthritis (PsA), the discovery of molecular players and biomarkers of disease activity or clinical remission achievement, remains unsolved. Metabolomics is a valuable technology in identifying biomarkers, by the study of a set of small molecules produced by the catabolism or anabolism of an organism in response to physiological or pathological states, allowing to better understand the disease-related variations downstream of the genome and proteome. <h3>Objectives</h3> In this pilot study, we have compared the serum metabolomics profile of patients with psoriatic arthritis with active or clinically inactive disease state compared with healty controls, using Nuclear Magnetic Resonance Spectroscopy (<sup>1</sup>H-NMR). <h3>Methods</h3> From a cohort of 300 PsA patients according to CASPAR criteria, we selected 30 PsA with active disease state by DAPSA &gt; 14 score (no bDMARDs ongoing) (A), 38 patients (peripheral arthritis subset) with &gt;1-year remission by antiTNFα assessed by DAPSA ≤ 4 (B) and 32 healthy controls (C) matching for mean age and gender ratio. The sera metabolomics profile of 100 subjects was analyzed with a Varian UNITY INOVA 500 MHz NMR spectrometer, combined with Multivariate statistical Analysis (MVA). Principal Component Analysis (PCA) and Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) were applied. The model's goodness was evaluated using a permutation test (Q2 intercept value &lt;0.005). <h3>Results</h3> The represented OPLS-DA models (Figure 1) exhibited a clear separation between subjects with active disease (red dots) and healthy controls (green dots), and patients with active disease or clinical remission state (blue dots), indicating significant differences in the serum metabolomics profile between all compared conditions. Interestingly, the OPLS-DA model shows how the PsA patients in the remission state have a profile which does not completely overlap with healthy subjects. The validity of the OPLS-DA models was evaluated through a permutation test using 500 times (Table 1) and indicate the great statistical validity of the OPLS-DA reported models. <h3>Conclusion</h3> The metabolomic profile of PsA patients with different disease state and healthy subjects reveal a high grade of dissimilarity between all compared conditions: remarkably, the metabolomic profile of patients with the drug-induced clinical remission appears still different from a healthy condition, perhaps reflecting molecular modifications induced by the great response to TNFalpha inhibition sustaining the clinical remission of PsA disease. Supplementary analysis of single-spectra is ongoing to obtain a list of metabolites differentially expressed in all conditions, and on this field further explorations are needed to validate metabolomics biomarkers that can accurately and reliably measure PsA disease activity or remission achievement, as well as to identify the metabolic fingerprint of antiTNFα-treatment success. <h3>Acknowledgements</h3> All staff of the Rheumatology Unit of the AOU of Cagliari, patients and volunteer donors. <h3>Disclosure of Interests</h3> None Declared.